CN112102297B - Method for identifying breaking fault of spring supporting plate of railway wagon bogie - Google Patents

Method for identifying breaking fault of spring supporting plate of railway wagon bogie Download PDF

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CN112102297B
CN112102297B CN202010982289.7A CN202010982289A CN112102297B CN 112102297 B CN112102297 B CN 112102297B CN 202010982289 A CN202010982289 A CN 202010982289A CN 112102297 B CN112102297 B CN 112102297B
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supporting plate
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CN112102297A (en
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马元通
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Harbin Kejia General Mechanical and Electrical Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G06T5/70—
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G06V10/751—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details
    • G06T2207/20032—Median filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30248—Vehicle exterior or interior
    • G06T2207/30268—Vehicle interior
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/08—Detecting or categorising vehicles

Abstract

A rail wagon bogie spring supporting plate fracture fault identification method belongs to the technical field of rail wagon bogie spring supporting plate fracture fault detection. The invention solves the problems of easy error detection and missed detection when the manual detection method is adopted to detect the breaking fault of the spring supporting plate. The high-definition imaging equipment on the two sides of the truck track is utilized to obtain high-definition images on the two sides of the truck. And acquiring a coarse positioning image containing a bogie spring supporting plate according to the axle base information, the bogie type and other prior knowledge. And finely positioning the region of the spring supporting plate which is easy to have faults by adopting a gradual positioning mode, extracting the edge of the supporting plate by utilizing an improved edge detection operator, judging whether the region is a fault according to edge information, and alarming the fault region if the region has the fault. The invention can be applied to the detection of the breaking fault of the spring supporting plate of the bogie.

Description

Method for identifying breaking fault of spring supporting plate of railway wagon bogie
Technical Field
The invention belongs to the technical field of rail wagon bogie spring supporting plate fracture fault detection, and particularly relates to a rail wagon bogie spring supporting plate fracture fault image identification method based on edge detection.
Background
The breakage of the spring supporting plate of the bogie is a fault mode of the spring supporting plate, and the spring supporting plate connects the left side frame and the right side frame together, so that the diamond-resistant rigidity of the bogie is improved. Meanwhile, the left and right side frames are respectively supported on the front and rear bearing saddles through top guide frame rocking seats to form a transversely synchronously-swinging suspension rod. When the two side frames swing, the swing bolster moves transversely, in order to limit overlarge transverse displacement of the swing bolster and prevent the swing bolster from jumping out, the spring supporting plate is provided with the stop plate, and the maximum transverse displacement of the swing bolster is limited through the matching of the stop and a triangular stop at the lower part of the swing bolster, so that the effect of safety stop is achieved. When the bogie spring supporting plate is broken, the function of the bogie spring supporting plate is influenced, and hidden danger is brought to the driving safety of a truck. The current manual vehicle inspection operation mode of looking at the images one by one has the problems of influence of personnel quality and responsibility, error and omission detection, difficulty in ensuring the operation quality, huge labor cost, low efficiency and the like.
Disclosure of Invention
The invention aims to solve the problems of easy error detection and missing detection when a manual detection method is adopted to detect the break fault of a spring supporting plate, and provides a method for identifying the break fault of the spring supporting plate of a railway freight car bogie.
The technical scheme adopted by the invention for solving the technical problems is as follows: a rail wagon bogie spring supporting plate breaking fault identification method comprises the following steps:
the method comprises the following steps of firstly, acquiring a linear array image of a truck;
step two, intercepting a local area image of the spring supporting plate from the image acquired in the step one;
step three, adopting a step-by-step positioning method to accurately position the baffles on the two sides of the spring supporting plate from the local area image obtained in the step two;
and step four, cutting the baffle plates on the two sides of the spring supporting plate into a plurality of sub-images with overlapped areas, and comparing gradient information of adjacent sub-images to judge whether the fracture fault occurs.
The invention has the beneficial effects that:
1. the automatic identification technology is introduced into truck fault detection, automatic fault identification and alarm are achieved, manual work is only needed to confirm alarm results, labor cost is effectively saved, and detection accuracy and detection efficiency are improved.
2. The spring supporting plate of different vehicle types has different forms, the position of a fault is not fixed, and the spring supporting plate is difficult to be accurately positioned directly by the traditional template matching or image processing method.
3. The image filtering is improved, a multi-sub-window filtering method is provided, and the interference caused by dust and dirt on the surface of a supporting plate during the running of a train is overcome.
4. According to the shape characteristics of the spring supporting plate, the edge detection operators are improved, two detection operators for edges of 45 degrees and 135 degrees are provided, so that the Sobel operator of the approach angle is adopted, and the edge detection effect is better.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of multiple sub-windows;
FIG. 3 is a schematic diagram of the Sobel operator at 45 °;
FIG. 4 is a schematic diagram of the Sobel operator at 135 deg.;
FIG. 5 is a schematic illustration of a cut of a two-sided baffle image.
Detailed Description
The first embodiment is as follows: this embodiment will be described with reference to fig. 1. The method for identifying the breaking fault of the spring supporting plate of the railway wagon bogie specifically comprises the following steps:
the method comprises the following steps of firstly, acquiring a linear array image of a truck;
step two, intercepting a local area image of the spring supporting plate from the image acquired in the step one;
step three, adopting a step-by-step positioning method to accurately position the baffles on the two sides of the spring supporting plate from the local area image obtained in the step two;
and step four, cutting the baffle plates on the two sides of the spring supporting plate into a plurality of sub-images with overlapped areas, and comparing gradient information of adjacent sub-images to judge whether the fracture fault occurs.
The automatic detection of the breaking fault of the bogie spring supporting plate has important significance. By combining image processing and deep learning technologies, automatic fault identification and alarm are realized, and the quality and efficiency of vehicle inspection operation are effectively improved.
The second embodiment is as follows: the first difference between the present embodiment and the specific embodiment is: the linear array image is shot by high-definition equipment built around the freight car track, and seamless splicing of the image is achieved through a line scanning mode.
The present embodiment can obtain a two-dimensional image with a large field of view and high accuracy.
The third concrete implementation mode: the first difference between the present embodiment and the specific embodiment is: the specific process of the second step is as follows:
and after roughly positioning the bogie area where the spring supporting plate is located according to the truck wheel base information and the bogie information, intercepting a local area image containing the spring supporting plate from the linear array image.
The local area image of the spring supporting plate is cut out, so that the time required by fault identification can be effectively reduced, and the identification accuracy is improved.
The fourth concrete implementation mode: the first difference between the present embodiment and the specific embodiment is: the specific process of the third step is as follows:
step three, positioning the spring supporting plate center row
Extracting the position information of a shadow area in a local area image according to the image characteristics of the shadow area in two symmetrical triangular holes on a bogie;
taking the symmetrical central columns of the two shaded areas as central columns of the spring supporting plate;
since the two triangular holes are symmetrical to the central row of the entire bogie, the two central rows of the symmetrical shaded areas are used as the central row of the entire bogie. Then, because the center row of the spring supporting plate and the center row of the bogie are at the same position, the center row of the spring supporting plate is successfully and precisely positioned;
step three and two, positioning the bottom edge of the spring supporting plate
Filtering the local area image by adopting a multi-sub-window filtering method to obtain a filtered image; extracting edge information of the U-shaped bottom edge of the spring supporting plate by using a Sobel operator to obtain position information of the bottom edge of the spring supporting plate;
step three, positioning the corner of the spring supporting plate
Detecting the left corner of the spring supporting plate by adopting a 45-degree Sobel operator, and detecting the right corner of the spring supporting plate by adopting a 135-degree Sobel operator to obtain position information of two corners of the spring supporting plate;
because the spring supporting plate breakage fault often occurs at the two side baffles of the U shape, the two side baffles need to be further positioned and extracted. The positions of the two side baffles can be determined by the positions of the bottom edge of the spring supporting plate and the connecting angles of the two side baffles.
According to the central column of the spring supporting plate and the bottom edge position of the spring supporting plate, a subregion image comprising the bottom edge of the spring supporting plate and the connecting angles of the baffle plates at two sides can be intercepted. According to the invention, two improved Sobel operators are designed according to the gray scale characteristics of the connecting angles of the U-shaped bottom edge and the two side baffles. The traditional Sobel operator only comprises a horizontal Sobel operator and a vertical Sobel operator, and the traditional Sobel operator is strong correspondingly to the edge in the horizontal direction and the edge in the vertical direction and weak correspondingly to the inclined edge. Aiming at the characteristic that the connecting angle of the bottom edge and the baffle plates at two sides of the spring supporting plate presents a fillet, improved Sobel operators of 45 degrees and 135 degrees are designed, so that the Sobel operators of the approaching angle are adopted, the edge detection effect is better, and the method is shown in figures 3 and 4. And detecting the left round angle by using a 135-degree improved Sobel operator, and detecting the right round angle by using a 45-degree improved Sobel operator according to the position information of two corners of the spring supporting plate.
Step three and four, extracting baffle subgraphs on two sides of the spring supporting plate
And extracting two sub-images containing baffles at two sides according to the position information of the bottom edge and two corners of the spring supporting plate.
The fifth concrete implementation mode: the fourth difference between this embodiment and the specific embodiment is that: the multi-sub-window filtering method specifically comprises the following steps:
the 5 x 5 filter window is divided into 9 3 x 3 sub-windows, as shown in fig. 2, and a sub-window Z is calculatedi，jMedian N of pixel values of all points withini，j；
Ni，j＝Median(x∈Fi，j)
Wherein, Fi，jIs a sub-window Zi，jA set of pixel values of inner points, i 1, 2, 3, j 1, 2, 3; median (x is belonged to F)i，j) Representing the selection of a sub-window Zi，jA median value among pixel values of all inner points;
calculate the average v of the values in all sub-windows:
Figure BDA0002687995850000041
calculating the median value N of each sub-windowi，jAbsolute difference Fa from the mean value vi，j：
Fai，j＝|Ni，j-v|
Calculating the normalized weight w corresponding to the median in each sub-windowi，j：
Figure BDA0002687995850000042
Where TH is the absolute difference Fa of all sub-windowsi，jAverage value of (d);
and carrying out weighted addition on the median values of all the sub-windows and the corresponding weight values, wherein the addition result is used as a filtering output F (i, j):
F(i，j)＝∑Ni，jwi，j。
for parts of a railway wagon, due to the fact that the running environment is complex, interference such as dust and dirt often occurs, irregular noise points such as black and white, different areas and random brightness are reflected on an image, the noise interference is different from salt and pepper noise in the traditional sense, and the noise interference is difficult to eliminate only through median filtering, so that the improved multi-sub-window filtering method is provided.
The method effectively filters salt and pepper noise by performing median calculation on each sub-window, and eliminates black and white spot noise with a large area by performing weighted operation on all the sub-windows. Meanwhile, the complexity of the texture in the sub-window is reflected by calculating the absolute difference value between the median value and the average value of the sub-window, and the detail information of the image is effectively reserved by improving the weight of the sub-window with complex texture.
Experimental results show that the algorithm has better filtering performance on noise in the image, better maintains the details of the image and has better effect than that of the traditional filtering algorithm.
The sixth specific implementation mode: the first difference between the present embodiment and the specific embodiment is: the specific process of the step four is as follows:
cutting the baffle plate image on the left side of the spring supporting plate into 3 sub-images, wherein the height of each 3 sub-images is 50 pixels, the width of each 3 sub-images is 30 pixels, and 20% of overlapping areas exist between adjacent sub-images in the 3 sub-images;
detecting each sub-image by using a horizontal Sobel operator and a vertical Sobel operator respectively to obtain the horizontal gradient strength and the vertical gradient strength in each sub-image, and calculating the absolute value of the ratio of the horizontal gradient strength to the vertical gradient strength of each sub-image;
making difference between the corresponding absolute values of two adjacent subgraphs, if the absolute value of the difference making result of the two adjacent subgraphs is larger than 0.2, determining that the left baffle has the fracture fault, otherwise, determining that the absolute value of the difference making result of any two adjacent subgraphs is smaller than or equal to 0.2, and determining that the left baffle has no fracture fault;
and similarly, processing the baffle image on the right side of the spring supporting plate, and judging whether the baffle on the right side breaks down.
Because the gradient change of the adjacent positions is more obvious than that of the whole image when the fracture fault occurs, the baffle images at the two sides of the supporting plate are cut into a plurality of sub-images for detection, and the cutting schematic diagram is shown in fig. 5. If the absolute value of the ratio of the horizontal gradient to the vertical gradient of the sub-graph a1 is a1, the absolute value of the ratio of the horizontal gradient to the vertical gradient of the sub-graph a2 is a2, and the sub-graph a1 and the sub-graph a2 are any adjacent sub-graphs, when the absolute value of the difference between a1 and a2 is greater than 0.2, the fracture fault is considered to occur, and the fracture fault cannot be considered to occur unless the absolute values of the difference results of the absolute values corresponding to any two adjacent sub-graphs are less than or equal to 0.2.
The above-described calculation examples of the present invention are merely to explain the calculation model and the calculation flow of the present invention in detail, and are not intended to limit the embodiments of the present invention. It will be apparent to those skilled in the art that other variations and modifications of the present invention can be made based on the above description, and it is not intended to be exhaustive or to limit the invention to the precise form disclosed, and all such modifications and variations are possible and contemplated as falling within the scope of the invention.

Claims (5)

1. A rail wagon bogie spring supporting plate breaking fault identification method is characterized by comprising the following steps:
the method comprises the following steps of firstly, acquiring a linear array image of a truck;
step two, intercepting a local area image of the spring supporting plate from the image acquired in the step one;
step three, adopting a step-by-step positioning method to accurately position the baffles on the two sides of the spring supporting plate from the local area image obtained in the step two; the specific process comprises the following steps:
step three, positioning the spring supporting plate center row
Extracting the position information of a shadow area in a local area image according to the image characteristics of the shadow area in two symmetrical triangular holes on a bogie;
taking the symmetrical central columns of the two shaded areas as central columns of the spring supporting plate;
step three and two, positioning the bottom edge of the spring supporting plate
Filtering the local area image by adopting a multi-sub-window filtering method to obtain a filtered image; extracting edge information of the U-shaped bottom edge of the spring supporting plate by using a Sobel operator to obtain position information of the bottom edge of the spring supporting plate;
step three, positioning the corner of the spring supporting plate
Detecting the left corner of the spring supporting plate by adopting a 45-degree Sobel operator, and detecting the right corner of the spring supporting plate by adopting a 135-degree Sobel operator to obtain position information of two corners of the spring supporting plate;
step three and four, extracting baffle subgraphs on two sides of the spring supporting plate
Extracting two sub-images comprising baffles on two sides according to the position information of the bottom edge and two corners of the spring supporting plate;
and step four, cutting the baffle plates on the two sides of the spring supporting plate into a plurality of sub-images with overlapped areas, and comparing gradient information of adjacent sub-images to judge whether the fracture fault occurs.
2. The method for identifying the breaking fault of the spring supporting plate of the railway freight car bogie as claimed in claim 1, wherein the linear array image is shot by high-definition equipment built around a railway freight car track, and seamless splicing of the image is realized in a line scanning mode.
3. The method for identifying the breaking fault of the spring supporting plate of the railway wagon bogie as claimed in claim 1, wherein the specific process of the second step is as follows:
and after roughly positioning the bogie area where the spring supporting plate is located according to the truck wheel base information and the bogie information, intercepting a local area image containing the spring supporting plate from the linear array image.
4. The method for identifying the breaking fault of the spring supporting plate of the railway wagon bogie as claimed in claim 1, wherein the multi-sub-window filtering method specifically comprises the following steps:
dividing a 5 × 5 filter window into 9 3 × 3 sub-windows, calculating a sub-window Zi,jMedian N of pixel values of all points withini，j；
Ni，j＝Median(x∈Fi，j)
Wherein, Fi，jIs a sub-window Zi，jSet of pixel values of inner points, Ni，jIs the median of the pixel values of all points in the sub-window, i is 1, 2, 3, j is 1, 2, 3;
calculate the average v of the values in all sub-windows:
Figure FDA0002972626930000021
calculating the median value N of each sub-windowi，jAbsolute difference Fa from the mean value vi，j：
Fai，j＝|Ni，j-v|
Calculating the normalized weight w corresponding to the median in each sub-windowi，j：
Figure FDA0002972626930000022
Where TH is the absolute difference Fa of all sub-windowsi，jAverage value of (d);
and carrying out weighted addition on the median values of all the sub-windows and the corresponding weight values, wherein the addition result is used as a filtering output F (i, j):
F(i，j)＝∑Ni，jwi，j。
5. the method for identifying the breaking fault of the spring supporting plate of the railway wagon bogie as claimed in claim 1, wherein the specific process of the step four is as follows:
cutting the baffle plate image on the left side of the spring supporting plate into 3 sub-images, wherein the height of each 3 sub-images is 50 pixels, the width of each 3 sub-images is 30 pixels, and 20% of overlapping areas exist between adjacent sub-images in the 3 sub-images;
detecting each sub-image by using a horizontal Sobel operator and a vertical Sobel operator respectively to obtain the horizontal gradient strength and the vertical gradient strength in each sub-image, and calculating the absolute value of the ratio of the horizontal gradient strength to the vertical gradient strength of each sub-image;
making difference between the corresponding absolute values of two adjacent subgraphs, if the absolute value of the difference making result of the two adjacent subgraphs is larger than 0.2, determining that the left baffle has the fracture fault, otherwise, determining that the absolute value of the difference making result of any two adjacent subgraphs is smaller than or equal to 0.2, and determining that the left baffle has no fracture fault;
and similarly, processing the baffle image on the right side of the spring supporting plate, and judging whether the baffle on the right side breaks down.
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